Predicting prostate cancer grade reclassification on active surveillance using a deep learning-based grading algorithm.

Autor:	Ding CC; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.; Current affiliation: Department of Pathology, University of California, San Francisco, San Francisco, CA, USA., Su ZT; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Erak E; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Oliveira LP; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Salles DC; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Jing Y; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Samanta P; AIRA Matrix Private Limited, Mumbai, Maharashtra, India., Bonthu S; AIRA Matrix Private Limited, Mumbai, Maharashtra, India., Joshi U; AIRA Matrix Private Limited, Mumbai, Maharashtra, India., Kondragunta C; AIRA Matrix Private Limited, Mumbai, Maharashtra, India., Singhal N; AIRA Matrix Private Limited, Mumbai, Maharashtra, India., De Marzo AM; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Trock BJ; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Pavlovich CP; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., de la Calle CM; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.; Current affiliation: Department of Urology, University of Washington, Seattle, WA, USA., Lotan TL; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Jazyk:	angličtina
Zdroj:	Journal of the National Cancer Institute [J Natl Cancer Inst] 2024 Oct 01; Vol. 116 (10), pp. 1683-1686.
DOI:	10.1093/jnci/djae139
Abstrakt:	Deep learning (DL)-based algorithms to determine prostate cancer (PCa) Grade Group (GG) on biopsy slides have not been validated by comparison to clinical outcomes. We used a DL-based algorithm, AIRAProstate, to regrade initial prostate biopsies in 2 independent PCa active surveillance (AS) cohorts. In a cohort initially diagnosed with GG1 PCa using only systematic biopsies (n = 138), upgrading of the initial biopsy to ≥GG2 by AIRAProstate was associated with rapid or extreme grade reclassification on AS (odds ratio = 3.3, P = .04), whereas upgrading of the initial biopsy by contemporary uropathologist reviews was not associated with this outcome. In a contemporary validation cohort that underwent prostate magnetic resonance imaging before initial biopsy (n = 169), upgrading of the initial biopsy (all contemporary GG1 by uropathologist grading) by AIRAProstate was associated with grade reclassification on AS (hazard ratio = 1.7, P = .03). These results demonstrate the utility of a DL-based grading algorithm in PCa risk stratification for AS. (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)
Databáze:	MEDLINE
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